library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(ggridges)
weather_df = 
  rnoaa::meteo_pull_monitors(
    c("USW00094728", "USC00519397", "USS0023B17S"),
    var = c("PRCP", "TMIN", "TMAX"), 
    date_min = "2017-01-01",
    date_max = "2017-12-31") %>%
  mutate(
    name = recode(
      id, 
      USW00094728 = "CentralPark_NY", 
      USC00519397 = "Waikiki_HA",
      USS0023B17S = "Waterhole_WA"),
    tmin = tmin / 10,
    tmax = tmax / 10) %>%
  select(name, id, everything())
## Registered S3 method overwritten by 'hoardr':
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##   print.cache_info httr
## using cached file: /Users/linhmaitran/Library/Caches/R/noaa_ghcnd/USW00094728.dly
## date created (size, mb): 2020-10-05 10:25:47 (7.522)
## file min/max dates: 1869-01-01 / 2020-10-31
## using cached file: /Users/linhmaitran/Library/Caches/R/noaa_ghcnd/USC00519397.dly
## date created (size, mb): 2020-10-05 10:25:55 (1.699)
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## using cached file: /Users/linhmaitran/Library/Caches/R/noaa_ghcnd/USS0023B17S.dly
## date created (size, mb): 2020-10-05 10:25:59 (0.88)
## file min/max dates: 1999-09-01 / 2020-10-31
weather_df
## # A tibble: 1,095 x 6
##    name           id          date        prcp  tmax  tmin
##    <chr>          <chr>       <date>     <dbl> <dbl> <dbl>
##  1 CentralPark_NY USW00094728 2017-01-01     0   8.9   4.4
##  2 CentralPark_NY USW00094728 2017-01-02    53   5     2.8
##  3 CentralPark_NY USW00094728 2017-01-03   147   6.1   3.9
##  4 CentralPark_NY USW00094728 2017-01-04     0  11.1   1.1
##  5 CentralPark_NY USW00094728 2017-01-05     0   1.1  -2.7
##  6 CentralPark_NY USW00094728 2017-01-06    13   0.6  -3.8
##  7 CentralPark_NY USW00094728 2017-01-07    81  -3.2  -6.6
##  8 CentralPark_NY USW00094728 2017-01-08     0  -3.8  -8.8
##  9 CentralPark_NY USW00094728 2017-01-09     0  -4.9  -9.9
## 10 CentralPark_NY USW00094728 2017-01-10     0   7.8  -6  
## # … with 1,085 more rows

Visualization with ggplot2

Basic scatterplot

To create a basic scatterplot, need to map variables to the X and Y coordinate aesthetics, add geoms to define

ggplot(weather_df, aes(x = tmin, y = tmax)) + geom_point()

#or use this code below to produce same figure:

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) +
  geom_point()

#save the output to an object and modify/print later:

plot_weather = 
  weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) 

plot_weather + geom_point()

The basic scatterplot gave some useful information - the variables are related roughly as we’d expect, and there aren’t any obvious outliers to investigate before moving on.

Advanced scatterplot - additional aesthetic mappings

name can be incorporated using the color aesthetic

#`name` can be incorporated using the `color` aesthetic

ggplot(weather_df, aes(x = tmin, y = tmax)) +
  geom_point(aes(color = name))

#add a smooth curve and make the data points more transparent.
ggplot(weather_df, aes(x = tmin, y = tmax)) +
  geom_point(aes(color = name), alpha = 0.5) +
  geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

The curve gives a sense of the relationship between variables, and the transparency shows where data are overlapping. However, the smooth curve is for all the data but the colors are only for the scatterplot. That is because X and Y mappings apply to the whole graphic, but color is currently geom-specific. I am having a hard time seeing everything on one plot, so I’m going to add facet based on name as well.

#add facet based on name

ggplot(weather_df, aes(x = tmin, y = tmax)) +
  geom_point(aes(color = name), alpha = 0.5) +
  geom_smooth(se = FALSE) +
  facet_grid(. ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

I’ve learned a lot about these data. However, the relationship between min and max temperature is now kinda boring, so I’d prefer something that shows the time of year. I also want to learn about precipitation.

ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
  geom_point(aes(size = prcp), alpha = 0.5) +
  geom_smooth(se = FALSE) + 
  facet_grid(. ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Learning Assessment 1

Write a code chain focuses only on Central park, converts temperatures to Fahrenheit, make a scatterplot of min vs. max temperature, and overlay a linear regression line using options in geom_smooth()

#this is my solution
library(dplyr)
ggplot(weather_df %>% 
  filter(name == "CentralPark_NY"), 
  aes(x = tmin, y = tmax)) +
  geom_point(aes(color = name)) + 
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

#this is the right solution
weather_df %>% 
  filter(name == "CentralPark_NY") %>% 
  mutate(
    tmax_fahr = tmax * (9 / 5) + 32,
    tmin_fahr = tmin * (9 / 5) + 32) %>% 
  ggplot(aes(x = tmin_fahr, y = tmax_fahr)) +
  geom_point(alpha = .5) + 
  geom_smooth(method = "lm", se = FALSE)
## `geom_smooth()` using formula 'y ~ x'

#Looks like there's a pretty linear relationship between min and max temperature in Central Park.

Odds and Ends

# There are lots of ways to mix and match elements, depending on your goals
ggplot(weather_df, aes(x = date, y = tmax, color = name)) + 
  geom_smooth(se = FALSE) 
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).

#When you're making scatterplot with lots of data, there's a limit to how much you can avoid overplotting using alpha levels and transparency. In these cases, `geom_hex()`, `geom_bin2d()`, or `geom_density2d()` can be handy
library(ggplot2)
ggplot(weather_df, aes(x = tmax, y = tmin)) +
  geom_hex()
## Warning: Removed 15 rows containing non-finite values (stat_binhex).
## Warning: Computation failed in `stat_binhex()`:
##   Package `hexbin` required for `stat_binhex`.
##   Please install and try again.

#`color` worked for both geom_point() and geom_smooth() but `shape` only applies to points.

Learning Assessment 2

ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin), color = "blue")
## Warning: Removed 15 rows containing missing values (geom_point).

ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin, color = "blue"))
## Warning: Removed 15 rows containing missing values (geom_point).

#These lines don't produce the same result because in the 1st attempt, we're defining the color of the points by hand; in the 2nd attempt, we're implicitly creating a color variable that has the value "blue" everywhere; ggplot is then assigning colors according to this variable using the default color scheme.

Univariate plots

Look at the distribution of single variables - this is an issue of learning some new geoms, and some new aesthetics.

ggplot(weather_df, aes(x = tmax)) + 
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#Play around with bin width and set the fill color using an aesthetic mapping. 

ggplot(weather_df, aes(x = tmax, fill = name)) +
  geom_histogram(position = "dodge", binwidth = 2)

#`position = "dodge` places the bars for each group side-by-side, but it can be hard to understand. prefer density plots over histogram. 

ggplot(weather_df, aes(x = tmax, fill = name)) +
  geom_density(alpha = 0.4, adjust = 0.5, color = "blue")

#`adjust` similar to `bindwidth` parameter. alpha = transparency level = 0.4 to make sure all densities appear. Lastly, adding `geom_rug()` can be helpful to show the raw data in addition to the density.

#boxplot
ggplot(weather_df, aes(x = name, y = tmax)) + geom_boxplot()

#violin plots
ggplot(weather_df, aes(x = name, y = tmax)) + 
  geom_violin(aes(fill = name), alpha = .5) + 
  stat_summary(fun = "median", color = "blue")

#ridge plots - nice if you have lots of categories in which the shape of the distribution matters. 
ggplot(weather_df, aes(x = tmax, y = name)) + 
  geom_density_ridges(scale = .85)
## Picking joint bandwidth of 1.84

Learning Assessment 3

#Make plots that compare precipitation across location. Try histogram, density plot, boxplot, violin plot and ridgeplot

#density plot
ggplot(weather_df, aes(x = prcp)) + 
  geom_density(aes(fill = name), alpha = .5) 
## Warning: Removed 3 rows containing non-finite values (stat_density).